Abstract The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.

中文翻译：

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基于自适应谐振频带提取的滚动轴承复合故障识别

摘要 高频共振（HFR）技术被认为是滚动轴承故障诊断的有力工具。与 HFR 在单一故障中的使用不同，复合故障和外来随机脉冲下的多个谐振频带的确定仍然是一项具有挑战性的任务。本文提出了一种基于自适应谐振频带提取的复合故障识别新方法。首先提出改进的冗余第二代小波包变换以将振动信号分解为各种窄带，以提供故障特征的精细分离。然后设计平方包络谱稀疏准则来量化埋藏在窄频带中的故障特征。最后，构造平方包络谱稀疏图以突出最佳谐振带，通过带通滤波和包络分析可以很好地检测复合故障。数值和实验结果证实了所提出方法的有效性和优越性，该方法对故障相关的脉冲更敏感，对外部干扰更稳健。